基于长短期记忆网络的电力系统量测缺失数据恢复方法  

Recovery Method for Missing Measurement Data of Power Systems Based on Long Short-Term Memory Networks

作　　者：王子馨 胡俊杰 刘宝柱[1] WANG Zixin;HU Junjie;LIU Baozhu(School of Electric and Electronic Engineering,North China Electric Power University,Beijing 102206,China)

机构地区：[1]华北电力大学电气与电子工程学院,北京市102206

出　　处：《电力建设》2021年第5期1-8,共8页Electric Power Construction

基　　金：国家自然科学基金项目(51877078);国家电网有限公司总部科技项目“城市综合能源系统的灵活性建模与优化技术研究”(SGJX0000KXJS1900321)。

摘　　要：随着电力系统规模不断增大,电力系统量测数据呈现快速增长趋势。然而海量数据的采集、测量、传输和存储等过程均可能出现数据缺失问题,从而威胁电网安全。针对电力系统量测缺失数据问题,文章提出了一种基于长短期记忆(long short-term memory,LSTM)网络的缺失数据恢复方法。首先,基于LSTM网络具有提取电力系统量测数据时序规律的特性,提出一种双层全连接LSTM网络模型,利用已知数据建立对缺失数据的映射。其次,为提高系统不同数据状态下的恢复精度,提出了一种随机森林状态辨识方法和考虑缺失数据位置的恢复策略。最后,利用仿真数据和实测数据验证该方法的有效性和准确性,结果表明该方法无需系统拓扑参数即可显著提高电力系统量测数据质量。As the scale of power systems continues to increase,measurement data of power system grows rapidly.However,the process of massive data collection,measurement,transmission,and storage in power systems may lose data,which seriously threatens the safety of the power grid.Aiming at the problem of missing data in power systems,this paper proposes a missing data recovery method based on long short-term memory(LSTM)networks.Firstly,since the LSTM network can extract the characteristics of the timing law of measurement data,a double-layer and full connection LSTM network architecture is presented,which uses known data to map missing data.Furthermore,in order to improve the recovery accuracy of missing data in different states,a state identification method based on random forest and a recovery strategy considering the location of the missing data are developed.Finally,case studies by using simulation data and measurement data verify the effectiveness and accuracy of the method proposed in this paper,and the proposed method can significantly improve the data quality of the power systems without system topology modeling.

关 键 词：电力系统 量测缺失数据恢复 长短期记忆网络 随机森林 

分 类 号：TM73[电气工程—电力系统及自动化]